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首页> 外文期刊>International journal of autonomous and adaptive communications systems >Study on microblog public opinion data mining algorithm based on multi-visual clustering model
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Study on microblog public opinion data mining algorithm based on multi-visual clustering model

机译:基于多视觉聚类模型的微博舆论数据挖掘算法研究

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摘要

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.
机译:由于微博舆论数据的随机分布,难以挖掘,本文提出了一种基于多视觉聚类模型的微博舆论数据挖掘算法。它构建了微博舆论数据的相位空间分布结构模型,模糊关联规则分布集微博公共意见数据,分析了微博舆论数据的大阶统计特征,并根据该数据的高阶统计特征,并根据型号推进模糊聚类中心的数据不同的统计特征行分区块调度。在虚拟数据库中重建了微博舆论数据的二进制结构,并且根据重建结果执行多角度模糊聚类,以实现微博公共观察数据的优化挖掘。仿真结果表明,该方法的采矿时间高达4.13毫秒,采矿精度高达100%。

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